Scenic Tourist Route Planning Algorithm Based on Mobile Computing and Grey Entropy Decision Model

Abstract

Since the concept of “Smart Earth” was put forward, countries have attached great importance to the research and introduction of smart tourism. With the increasing updating of information technology, the tourism industry is also constantly trying to transform intelligently, and the personalized recommendation of its travel routes has become the most important development in smart tourism, one of the hot topics. Tourism agencies create traditional travel itineraries based on the preferences of the majority of people and the characteristics of tourist attractions. These lines are primarily used for team travel. People’s expectations for travel route planning are increasing as their living standards rise, and the traditional route planning algorithm based on a static road network data model has struggled to describe the complex and changing scenic environment. The improved random walk algorithm suggests suitable travel routes to users, allowing for more accurate route recommendations and effectively addressing the problem of new route recommendation difficulty. This paper proposes a scenic tourist route planning algorithm using the grey entropy decision-making model and mobile computing. The content of the recommended results is more closely related to the target recommendation user’s tendency after personalized adjustment, which has a better effect on the personalization of the recommended results.